Ridge estimation of the VAR(1) model and its time series chain graph from multivariate time-course omics data

ABSTRACTNew technologies have given rise to an abundance of -omics data, particularly metabolomics data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of new computational visualization methodologies. Here, we present a new method for the visualization of time-course metabolomics data within the context of metabolic network maps. We demonstrate the utility of this method by examining previously published data for two cellular systems—the human platelet and erythrocyte under cold storage for use in transfusion medicine.The results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation which mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures.In conclusion, this new visualization technique introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types.AUTHOR SUMMARYProfiling the dynamic state of a metabolic network through the use of time-course metabolomics technologies allows insights into cellular biochemistry. Interpreting these data together at the systems level provides challenges that can be addressed through the development of new visualization approaches. Here, we present a new method for the visualization of time-course metabolomics data that integrates data into an existing metabolic network map. In brief, the metabolomics data are visualized directly on a network map with dynamic elements (nodes that either change size, fill level, or color corresponding with the concentration) while the user controls the time series (i.e., which time point is being displayed) through a graphical interface. We provide short videos that illustrate the utility of this method through its application to existing data sets for the human platelet and erythrocyte. The results presented here give blueprints for the development of visualization methods for other time-course -omics data types that attempt to understand systems-level physiology.

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0311 - Effect of ammonia on the dynamics of anaerobic digestion microbiome: omics data integration in a time-course context

10.26226/morressier.5b5199beb1b87b000ecee94b ◽

2018 ◽

Author(s):

Olivier Chapleur

Keyword(s):

Anaerobic Digestion ◽

Data Integration ◽

Time Course ◽

Omics Data ◽

Omics Data Integration

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A comparison of methods for smoothing and gap filling time series of remote sensing observations – application to MODIS LAI products

Biogeosciences ◽

10.5194/bg-10-4055-2013 ◽

2013 ◽

Vol 10 (6) ◽

pp. 4055-4071 ◽

Cited By ~ 84

Author(s):

S. Kandasamy ◽

F. Baret ◽

A. Verger ◽

P. Neveux ◽

M. Weiss

Keyword(s):

Time Series ◽

Missing Data ◽

Time Course ◽

Singular Spectrum Analysis ◽

Gaussian Function ◽

Gap Filling ◽

Area Index ◽

Filling Time ◽

Moderate Resolution ◽

Temporal Profiles

Abstract. Moderate resolution satellite sensors including MODIS (Moderate Resolution Imaging Spectroradiometer) already provide more than 10 yr of observations well suited to describe and understand the dynamics of earth's surface. However, these time series are associated with significant uncertainties and incomplete because of cloud cover. This study compares eight methods designed to improve the continuity by filling gaps and consistency by smoothing the time course. It includes methods exploiting the time series as a whole (iterative caterpillar singular spectrum analysis (ICSSA), empirical mode decomposition (EMD), low pass filtering (LPF) and Whittaker smoother (Whit)) as well as methods working on limited temporal windows of a few weeks to few months (adaptive Savitzky–Golay filter (SGF), temporal smoothing and gap filling (TSGF), and asymmetric Gaussian function (AGF)), in addition to the simple climatological LAI yearly profile (Clim). Methods were applied to the MODIS leaf area index product for the period 2000–2008 and over 25 sites showed a large range of seasonal patterns. Performances were discussed with emphasis on the balance achieved by each method between accuracy and roughness depending on the fraction of missing observations and the length of the gaps. Results demonstrate that the EMD, LPF and AGF methods were failing because of a significant fraction of gaps (more than 20%), while ICSSA, Whit and SGF were always providing estimates for dates with missing data. TSGF (Clim) was able to fill more than 50% of the gaps for sites with more than 60% (80%) fraction of gaps. However, investigation of the accuracy of the reconstructed values shows that it degrades rapidly for sites with more than 20% missing data, particularly for ICSSA, Whit and SGF. In these conditions, TSGF provides the best performances that are significantly better than the simple Clim for gaps shorter than about 100 days. The roughness of the reconstructed temporal profiles shows large differences between the various methods, with a decrease of the roughness with the fraction of missing data, except for ICSSA. TSGF provides the smoothest temporal profiles for sites with a % gap > 30%. Conversely, ICSSA, LPF, Whit, AGF and Clim provide smoother profiles than TSGF for sites with a % gap < 30%. Impact of the accuracy and smoothness of the reconstructed time series were evaluated on the timing of phenological stages. The dates of start, maximum and end of the season are estimated with an accuracy of about 10 days for the sites with a % gap < 10% and increases rapidly with the % gap. TSGF provides more accurate estimates of phenological timing up to a % gap < 60%.

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Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab021 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Hitoshi Iuchi ◽

Michiaki Hamada

Keyword(s):

Gene Expression ◽

Time Series ◽

Time Course ◽

Time Series Data ◽

Expression Patterns ◽

Detection Methods ◽

Series Data ◽

Expression Levels ◽

Over Time ◽

Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

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Inferring Mechanism of Action of an Unknown Compound from Time Series Omics Data

Computational Methods in Systems Biology - Lecture Notes in Computer Science ◽

10.1007/978-3-319-99429-1_14 ◽

2018 ◽

pp. 238-255 ◽

Cited By ~ 2

Author(s):

Akos Vertes ◽

Albert-Baskar Arul ◽

Peter Avar ◽

Andrew R. Korte ◽

Hang Li ◽

...

Keyword(s):

Time Series ◽

Mechanism Of Action ◽

Omics Data ◽

Unknown Compound

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P–368 Dynamic metabolomic profiling during early implantation period

Human Reproduction ◽

10.1093/humrep/deab130.367 ◽

2021 ◽

Vol 36 (Supplement_1) ◽

Author(s):

Y Zhang ◽

Y W Zhao ◽

C C Wang ◽

T C Li

Keyword(s):

Mass Spectrometry ◽

Time Series ◽

Embryo Transfer ◽

Study Design ◽

Empirical Bayes ◽

Time Course ◽

Time Series Data ◽

Series Data ◽

Metabolomic Profiling ◽

Implantation Period

Abstract Study question To investigate the different metabolomic profiling in serum between pregnant and non-pregnant women during early implantation period. Summary answer Metabolomics of progesterone-related hormones enhances from ET day3 for pregnancy women compared with non-pregnancy women. What is known already Metabolomics is based on high-throughput analytical methods to identify and quantify metabolites. Compared to other omics study, metabolomics is the closest one to the phenotype, allowing the observation of dynamic changes in phenotype at specific timepoints. So far there is no published work about the metabolomics profile in human early implantation period. Study design, size, duration: Study design: comparative study. Size: 14 pregnancy women and 14 non-pregnancy women. duration: time-course. Participants/materials, setting, methods Participants: pregnancy women and unpregnancy women after embryo transfer (ET). Setting: university-based study. Methods: Peripheral blood were collected at ET day0, 3, 6 and 9. metabolomic profiling in serum by platforms of capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography–mass spectrometry (LC-MS). Main results and the role of chance There were no statistical difference of the age, BMI, basal FSH level, endometrium thickness on the day of embryo transfer, distribution of primary and secondary fertility, embryo transfer cycle as well as the infertile types between the two groups. After deleting those with over 50% missing data, we finally have 310 metabolites into statistical analysis. Among the 310 metabolite, lipid metabolites account the largest percentage, nearly half of all metabolites. The second biggest class of metabolites in our data was organic acids. Combined results in repeated measurement ANOVA (RM-ANOVA) and ANOVA-simultaneous component analysis (ASCA) as well as multivariate empirical Bayes time-series analysis (MEBA), we finally found that progesterone-related hormones were the most important metabolites for the whole time-series data. Those significant metabolites showed a significant down regulation from ET day0 to ET day3 and up regulation from ET day3 to ET day9. Limitations, reasons for caution we have limited sample size for this study and further validation is necessary for confirmation. Wider implications of the findings: The phenomenon of upregulation of progesterone-related hormones from day3 in pregnancy group might be related to the embryo-originated hcg. Because the embryo has entered into endometrium at day3 and produced cytokines, hcg and other interaction with endometrium. Trial registration number NA

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Processing of Natural Time Series of Intensities in the Early Visual System of the Blowfly

Perception ◽

10.1068/v970023 ◽

1997 ◽

Vol 26 (1_suppl) ◽

pp. 24-24 ◽

Cited By ~ 1

Author(s):

J H van Hateren

Keyword(s):

Time Series ◽

Visual System ◽

Visual Processing ◽

Time Course ◽

Temporal Frequency ◽

Power Spectra ◽

Calliphora Vicina ◽

Intensity Measurements ◽

Natural Time ◽

Early Visual System

The first steps of processing in the visual system of the blowfly are well suited for studying the relationship between the properties of the environment and the function of visual processing (eg Srinivasan et al, 1982 Proceedings of the Royal Society, London B216 427; van Hateren, 1992 Journal of Comparative Physiology A171 157). Although the early visual system appears to be linear to some extent, there are also reports on functionally significant nonlinearities (Laughlin, 1981 Zeitschrift für Naturforschung36c 910). Recent theories using information theory for understanding the early visual system perform reasonably well, but not quite as well as the real visual system when confronted with natural stimuli [eg van Hateren, 1992 Nature (London)360 68]. The main problem seems to be that they lack a component that adapts with the right time course to changes in stimulus statistics (eg the local average light intensity). In order to study this problem of adaptation with a relatively simple, yet realistic, stimulus I recorded time series of natural intensities, and played them back via a high-brightness LED to the visual system of the blowfly ( Calliphora vicina). The power spectra of the intensity measurements and photoreceptor responses behave approximately as 1/ f, with f the temporal frequency, whilst those of second-order neurons (LMCs) are almost flat. The probability distributions of the responses of LMCs are almost gaussian and largely independent of the input contrast, unlike the distributions of photoreceptor responses and intensity measurements. These results suggest that LMCs are in effect executing a form of contrast normalisation in the time domain.

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Assessment of varicella vaccine effectiveness in Germany: a time-series approach

Epidemiology and Infection ◽

10.1017/s0950268810002815 ◽

2010 ◽

Vol 139 (11) ◽

pp. 1710-1719 ◽

Cited By ~ 18

Author(s):

M. HÖHLE ◽

A. SIEDLER ◽

H.-M. BADER ◽

M. LUDWIG ◽

U. HEININGER ◽

...

Keyword(s):

Time Series ◽

Vaccination Programme ◽

Time Course ◽

Vaccine Coverage ◽

Multivariate Time Series ◽

Varicella Vaccine ◽

Vaccine Effectiveness ◽

Childhood Vaccination ◽

Time Series Regression ◽

Sentinel Network

SUMMARYA multivariate time-series regression model was developed in order to describe the 2005–2008 age-specific time-course of varicella sentinel surveillance data following the introduction of a varicella childhood vaccination programme in Germany. This ecological approach allows the assessment of vaccine effectiveness under field conditions by relating vaccine coverage in cohorts of 24-month-old children to the mean number of cases per reporting unit in the sentinel network. For the 1–2 years age group, which is directly affected by the vaccination programme, a one-dose vaccine effectiveness of 83·2% (95% CI 80·2–85·7) was estimated which corresponds to previous approaches assessing varicella vaccine effectiveness in the field in the USA.

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Inferring phage-bacteria infection networks from time series data

10.1101/051581 ◽

2016 ◽

Author(s):

Luis F. Jover ◽

Justin Romberg ◽

Joshua S. Weitz

Keyword(s):

Time Series ◽

Time Course ◽

Community Dynamics ◽

Time Series Data ◽

Plaque Assay ◽

Interaction Network ◽

Series Data ◽

Factors Affecting ◽

Evolutionary Changes

In communities with bacterial viruses (phage) and bacteria, the phage-bacteria infection network establishes which virus types infects which host types. The structure of the infection network is a key element in understanding community dynamics. Yet, this infection network is often difficult to ascertain. Introduced over 60 years ago, the plaque assay remains the gold-standard for establishing who infects whom in a community. This culture-based approach does not scale to environmental samples with increased levels of phage and bacterial diversity, much of which is currently unculturable. Here, we propose an alternative method of inferring phage-bacteria infection networks. This method uses time series data of fluctuating population densities to estimate the complete interaction network without having to test each phage-bacteria pair individually. We use in silico experiments to analyze the factors affecting the quality of network reconstruction and find robust regimes where accurate reconstructions are possible. In addition, we present a multi-experiment approach where time series from different experiments are combined to improve estimates of the infection network and mitigate against the possibility of evolutionary changes to infection during the time-course of measurement.

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